!5297 CutMixBatch and MixUpBatch fix for 2D one-hot labels

Merge pull request !5297 from MahdiRahmaniHanzaki/cutmix-fix

mindspore/ccsrc/minddata/dataset/kernels/image/cutmix_batch_op.cc

@@ -59,10 +59,17 @@ Status CutMixBatchOp::Compute(const TensorRow &input, TensorRow *output) {
 
   // Check inputs
   if (image_shape.size() != 4 || image_shape[0] != label_shape[0]) {
-    RETURN_STATUS_UNEXPECTED("You must make sure images are HWC or CHW and batched before calling CutMixBatch.");
+    RETURN_STATUS_UNEXPECTED(
+      "CutMixBatch: You must make sure images are HWC or CHW and batched before calling CutMixBatch.");
   }
-  if (label_shape.size() != 2) {
-    RETURN_STATUS_UNEXPECTED("CutMixBatch: Label's must be in one-hot format and in a batch.");
+  if (!input.at(1)->type().IsInt()) {
+    RETURN_STATUS_UNEXPECTED("CutMixBatch: Wrong labels type. The second column (labels) must only include int types.");
+  }
+  if (label_shape.size() != 2 && label_shape.size() != 3) {
+    RETURN_STATUS_UNEXPECTED(
+      "CutMixBatch: Wrong labels shape. The second column (labels) must have a shape of NC or NLC where N is the batch "
+      "size, L is the number of labels in each row, "
+      "and C is the number of classes. labels must be in one-hot format and in a batch.");
   }
   if ((image_shape[1] != 1 && image_shape[1] != 3) && image_batch_format_ == ImageBatchFormat::kNCHW) {
     RETURN_STATUS_UNEXPECTED("CutMixBatch: Image doesn't match the given image format.");
@@ -84,10 +91,12 @@ Status CutMixBatchOp::Compute(const TensorRow &input, TensorRow *output) {
 
   // Tensor holding the output labels
   std::shared_ptr<Tensor> out_labels;
-  RETURN_IF_NOT_OK(Tensor::CreateEmpty(TensorShape(label_shape), DataType(DataType::DE_FLOAT32), &out_labels));
+  RETURN_IF_NOT_OK(TypeCast(std::move(input.at(1)), &out_labels, DataType(DataType::DE_FLOAT32)));
 
+  int64_t row_labels = label_shape.size() == 3 ? label_shape[1] : 1;
+  int64_t num_classes = label_shape.size() == 3 ? label_shape[2] : label_shape[1];
   // Compute labels and images
-  for (int i = 0; i < image_shape[0]; i++) {
+  for (int64_t i = 0; i < image_shape[0]; i++) {
     // Calculating lambda
     // If x1 is a random variable from Gamma(a1, 1) and x2 is a random variable from Gamma(a2, 1)
     // then x = x1 / (x1+x2) is a random variable from Beta(a1, a2)
@@ -138,22 +147,29 @@ Status CutMixBatchOp::Compute(const TensorRow &input, TensorRow *output) {
       }
 
       // Compute labels
-      for (int j = 0; j < label_shape[1]; j++) {
-        if (input.at(1)->type().IsSignedInt()) {
-          int64_t first_value, second_value;
-          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
-          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i] % label_shape[0], j}));
-          RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, label_lam * first_value + (1 - label_lam) * second_value));
-        } else {
-          uint64_t first_value, second_value;
-          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
-          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i] % label_shape[0], j}));
-          RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, label_lam * first_value + (1 - label_lam) * second_value));
+
+      for (int64_t j = 0; j < row_labels; j++) {
+        for (int64_t k = 0; k < num_classes; k++) {
+          std::vector<int64_t> first_index = label_shape.size() == 3 ? std::vector{i, j, k} : std::vector{i, k};
+          std::vector<int64_t> second_index =
+            label_shape.size() == 3 ? std::vector{rand_indx[i], j, k} : std::vector{rand_indx[i], k};
+          if (input.at(1)->type().IsSignedInt()) {
+            int64_t first_value, second_value;
+            RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, first_index));
+            RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, second_index));
+            RETURN_IF_NOT_OK(
+              out_labels->SetItemAt(first_index, label_lam * first_value + (1 - label_lam) * second_value));
+          } else {
+            uint64_t first_value, second_value;
+            RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, first_index));
+            RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, second_index));
+            RETURN_IF_NOT_OK(
+              out_labels->SetItemAt(first_index, label_lam * first_value + (1 - label_lam) * second_value));
+          }
         }
       }
     }
   }
-
   std::shared_ptr<Tensor> out_images;
   RETURN_IF_NOT_OK(TensorVectorToBatchTensor(images, &out_images));
 

mindspore/ccsrc/minddata/dataset/kernels/image/mixup_batch_op.cc

@@ -38,10 +38,17 @@ Status MixUpBatchOp::Compute(const TensorRow &input, TensorRow *output) {
 
   // Check inputs
   if (image_shape.size() != 4 || image_shape[0] != label_shape[0]) {
-    RETURN_STATUS_UNEXPECTED("You must make sure images are HWC or CHW and batched before calling MixUpBatch.");
+    RETURN_STATUS_UNEXPECTED(
+      "MixUpBatch:You must make sure images are HWC or CHW and batched before calling MixUpBatch.");
   }
-  if (label_shape.size() != 2) {
-    RETURN_STATUS_UNEXPECTED("MixUpBatch: Label's must be in one-hot format and in a batch.");
+  if (!input.at(1)->type().IsInt()) {
+    RETURN_STATUS_UNEXPECTED("MixUpBatch: Wrong labels type. The second column (labels) must only include int types.");
+  }
+  if (label_shape.size() != 2 && label_shape.size() != 3) {
+    RETURN_STATUS_UNEXPECTED(
+      "MixUpBatch: Wrong labels shape. The second column (labels) must have a shape of NC or NLC where N is the batch "
+      "size, L is the number of labels in each row, "
+      "and C is the number of classes. labels must be in one-hot format and in a batch.");
   }
   if ((image_shape[1] != 1 && image_shape[1] != 3) && (image_shape[3] != 1 && image_shape[3] != 3)) {
     RETURN_STATUS_UNEXPECTED("MixUpBatch: Images must be in the shape of HWC or CHW.");
@@ -65,23 +72,31 @@ Status MixUpBatchOp::Compute(const TensorRow &input, TensorRow *output) {
 
   // Compute labels
   std::shared_ptr<Tensor> out_labels;
-  RETURN_IF_NOT_OK(TypeCast(std::move(input.at(1)), &out_labels, DataType("float32")));
+  RETURN_IF_NOT_OK(TypeCast(std::move(input.at(1)), &out_labels, DataType(DataType::DE_FLOAT32)));
+
+  int64_t row_labels = label_shape.size() == 3 ? label_shape[1] : 1;
+  int64_t num_classes = label_shape.size() == 3 ? label_shape[2] : label_shape[1];
+
   for (int64_t i = 0; i < label_shape[0]; i++) {
-    for (int64_t j = 0; j < label_shape[1]; j++) {
-      if (input.at(1)->type().IsSignedInt()) {
-        int64_t first_value, second_value;
-        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
-        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i], j}));
-        RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, lam * first_value + (1 - lam) * second_value));
-      } else {
-        uint64_t first_value, second_value;
-        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
-        RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i], j}));
-        RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, lam * first_value + (1 - lam) * second_value));
+    for (int64_t j = 0; j < row_labels; j++) {
+      for (int64_t k = 0; k < num_classes; k++) {
+        std::vector<int64_t> first_index = label_shape.size() == 3 ? std::vector{i, j, k} : std::vector{i, k};
+        std::vector<int64_t> second_index =
+          label_shape.size() == 3 ? std::vector{rand_indx[i], j, k} : std::vector{rand_indx[i], k};
+        if (input.at(1)->type().IsSignedInt()) {
+          int64_t first_value, second_value;
+          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, first_index));
+          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, second_index));
+          RETURN_IF_NOT_OK(out_labels->SetItemAt(first_index, lam * first_value + (1 - lam) * second_value));
+        } else {
+          uint64_t first_value, second_value;
+          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, first_index));
+          RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, second_index));
+          RETURN_IF_NOT_OK(out_labels->SetItemAt(first_index, lam * first_value + (1 - lam) * second_value));
+        }
       }
     }
   }
-
   // Compute images
   for (int64_t i = 0; i < images.size(); i++) {
     TensorShape remaining({-1});

tests/ut/python/dataset/test_cutmix_batch_op.py

@@ -27,6 +27,7 @@ from util import save_and_check_md5, diff_mse, visualize_list, config_get_set_se
 
 DATA_DIR = "../data/dataset/testCifar10Data"
 DATA_DIR2 = "../data/dataset/testImageNetData2/train/"
+DATA_DIR3 = "../data/dataset/testCelebAData/"
 
 GENERATE_GOLDEN = False
 
@@ -36,7 +37,6 @@ def test_cutmix_batch_success1(plot=False):
     Test CutMixBatch op with specified alpha and prob parameters on a batch of CHW images
     """
     logger.info("test_cutmix_batch_success1")
-
     # Original Images
     ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
     ds_original = ds_original.batch(5, drop_remainder=True)
@@ -164,6 +164,53 @@ def test_cutmix_batch_success3(plot=False):
     logger.info("MSE= {}".format(str(np.mean(mse))))
 
 
+def test_cutmix_batch_success4(plot=False):
+    """
+    Test CutMixBatch on a dataset where OneHot returns a 2D vector
+    """
+    logger.info("test_cutmix_batch_success4")
+
+    ds_original = ds.CelebADataset(DATA_DIR3, shuffle=False)
+    decode_op = vision.Decode()
+    ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
+    ds_original = ds_original.batch(2, drop_remainder=True)
+
+    images_original = None
+    for idx, (image, _) in enumerate(ds_original):
+        if idx == 0:
+            images_original = image
+        else:
+            images_original = np.append(images_original, image, axis=0)
+
+    # CutMix Images
+    data1 = ds.CelebADataset(dataset_dir=DATA_DIR3, shuffle=False)
+
+    decode_op = vision.Decode()
+    data1 = data1.map(input_columns=["image"], operations=[decode_op])
+
+    one_hot_op = data_trans.OneHot(num_classes=100)
+    data1 = data1.map(input_columns=["attr"], operations=one_hot_op)
+
+    cutmix_batch_op = vision.CutMixBatch(mode.ImageBatchFormat.NHWC, 0.5, 0.9)
+    data1 = data1.batch(2, drop_remainder=True)
+    data1 = data1.map(input_columns=["image", "attr"], operations=cutmix_batch_op)
+
+    images_cutmix = None
+    for idx, (image, _) in enumerate(data1):
+        if idx == 0:
+            images_cutmix = image
+        else:
+            images_cutmix = np.append(images_cutmix, image, axis=0)
+    if plot:
+        visualize_list(images_original, images_cutmix)
+
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_cutmix[i], images_original[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+
+
 def test_cutmix_batch_nhwc_md5():
     """
     Test CutMixBatch on a batch of HWC images with MD5:
@@ -368,7 +415,7 @@ def test_cutmix_batch_fail7():
                 images_cutmix = image
             else:
                 images_cutmix = np.append(images_cutmix, image, axis=0)
-    error_message = "CutMixBatch: Label's must be in one-hot format and in a batch"
+    error_message = "CutMixBatch: Wrong labels shape. The second column (labels) must have a shape of NC or NLC"
     assert error_message in str(error.value)
 
 
@@ -394,6 +441,7 @@ if __name__ == "__main__":
     test_cutmix_batch_success1(plot=True)
     test_cutmix_batch_success2(plot=True)
     test_cutmix_batch_success3(plot=True)
+    test_cutmix_batch_success4(plot=True)
     test_cutmix_batch_nchw_md5()
     test_cutmix_batch_nhwc_md5()
     test_cutmix_batch_fail1()

tests/ut/python/dataset/test_mixup_op.py

@@ -26,6 +26,7 @@ from util import save_and_check_md5, diff_mse, visualize_list, config_get_set_se
 
 DATA_DIR = "../data/dataset/testCifar10Data"
 DATA_DIR2 = "../data/dataset/testImageNetData2/train/"
+DATA_DIR3 = "../data/dataset/testCelebAData/"
 
 GENERATE_GOLDEN = False
 
@@ -162,6 +163,55 @@ def test_mixup_batch_success3(plot=False):
     logger.info("MSE= {}".format(str(np.mean(mse))))
 
 
+def test_mixup_batch_success4(plot=False):
+    """
+    Test MixUpBatch op on a dataset where OneHot returns a 2D vector.
+    Alpha parameter will be selected by default in this case
+    """
+    logger.info("test_mixup_batch_success4")
+
+    # Original Images
+    ds_original = ds.CelebADataset(DATA_DIR3, shuffle=False)
+    decode_op = vision.Decode()
+    ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
+    ds_original = ds_original.batch(2, drop_remainder=True)
+
+    images_original = None
+    for idx, (image, _) in enumerate(ds_original):
+        if idx == 0:
+            images_original = image
+        else:
+            images_original = np.append(images_original, image, axis=0)
+
+    # MixUp Images
+    data1 = ds.CelebADataset(DATA_DIR3, shuffle=False)
+
+    decode_op = vision.Decode()
+    data1 = data1.map(input_columns=["image"], operations=[decode_op])
+
+    one_hot_op = data_trans.OneHot(num_classes=100)
+    data1 = data1.map(input_columns=["attr"], operations=one_hot_op)
+
+    mixup_batch_op = vision.MixUpBatch()
+    data1 = data1.batch(2, drop_remainder=True)
+    data1 = data1.map(input_columns=["image", "attr"], operations=mixup_batch_op)
+
+    images_mixup = np.array([])
+    for idx, (image, _) in enumerate(data1):
+        if idx == 0:
+            images_mixup = image
+        else:
+            images_mixup = np.append(images_mixup, image, axis=0)
+    if plot:
+        visualize_list(images_original, images_mixup)
+
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_mixup[i], images_original[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+
+
 def test_mixup_batch_md5():
     """
     Test MixUpBatch with MD5:
@@ -218,7 +268,7 @@ def test_mixup_batch_fail1():
                 images_mixup = image
             else:
                 images_mixup = np.append(images_mixup, image, axis=0)
-        error_message = "You must make sure images are HWC or CHW and batch"
+        error_message = "You must make sure images are HWC or CHW and batched"
         assert error_message in str(error.value)
 
 
@@ -316,12 +366,50 @@ def test_mixup_batch_fail4():
         assert error_message in str(error.value)
 
 
+def test_mixup_batch_fail5():
+    """
+    Test MixUpBatch Fail 5
+    We expect this to fail because labels are not OntHot encoded
+    """
+    logger.info("test_mixup_batch_fail5")
+
+    # Original Images
+    ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
+    ds_original = ds_original.batch(5)
+
+    images_original = np.array([])
+    for idx, (image, _) in enumerate(ds_original):
+        if idx == 0:
+            images_original = image
+        else:
+            images_original = np.append(images_original, image, axis=0)
+
+    # MixUp Images
+    data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
+
+    mixup_batch_op = vision.MixUpBatch()
+    data1 = data1.batch(5, drop_remainder=True)
+    data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
+
+    with pytest.raises(RuntimeError) as error:
+        images_mixup = np.array([])
+        for idx, (image, _) in enumerate(data1):
+            if idx == 0:
+                images_mixup = image
+            else:
+                images_mixup = np.append(images_mixup, image, axis=0)
+    error_message = "MixUpBatch: Wrong labels shape. The second column (labels) must have a shape of NC or NLC"
+    assert error_message in str(error.value)
+
+
 if __name__ == "__main__":
     test_mixup_batch_success1(plot=True)
     test_mixup_batch_success2(plot=True)
     test_mixup_batch_success3(plot=True)
+    test_mixup_batch_success4(plot=True)
     test_mixup_batch_md5()
     test_mixup_batch_fail1()
     test_mixup_batch_fail2()
     test_mixup_batch_fail3()
     test_mixup_batch_fail4()
+    test_mixup_batch_fail5()